Skip to main content

Use Python functions and modules in R with the "reticulate" package

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Since its inception over 40 years ago, when S (R's predecessor) was just a sketch on John Chambers' wall at Bell Labs, R has always been a language for providing interfaces. I was reminded of this during Dirk Eddelbuettel's presentation at the Chicago R User Group meetup last night, where he enumerated Chambers' three principles behind its design (from his 2016 book, Extending R):

  1. Object: Everything that exists in R is an object
  2. Function: Everything that happens in R is a function call
  3. Interface: Interfaces to other software are a part of R

The third principle "Interface" is demonstrated by R's broad connections to data sources, numerical and statistical computation libraries, graphical systems, external applications, and other languages. And it's further supported by the formal announcement this week of the reticulate package from RStudio, which provides a new interface between R and Python. With reticulate, you can:

  • Import objects from Python, automatically converted into their equivalent R types. (For example, Pandas data frames become R data.frame objects, and NumPy arrays become R matrix objects.)
  • Import Python modules, and call their functions from R 
  • Source Python scripts from R
  • Interactively run Python commands from the R command line
  • Combine R code and Python code (and output) in R Markdown documents, as shown in the snippet below

Rmarkdown_engine_zoomed

The reticulate package was first released on Github in January 2017, but has only been available on CRAN since last month. It has already spawned several higher-level integrations between R and Python-based systems, including:

  • H204GPU, a R package for H20's GPU-based scikit-learn-like suite of algorithms;
  • greta, a packagefor Bayesian model estimation with Markov-chain Monte-carlo, based on Tensorflow
  • spacyr, a wrapper for the spaCy natural language processing toolkit; and
  • XRPython, John Chamber's interface to Python based on his XR package for language extensions to R, which now uses reticulate for its low-level interface to Python.

The reticulate package is available now on CRAN. You can find more details in the announcement at the link below.

RStudio blog: reticulate: R interface to Python

 

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


from R-bloggers https://ift.tt/2GKd7mB
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Using RStudio and LaTeX

(This article was first published on r – Experimental Behaviour , and kindly contributed to R-bloggers) This post will explain how to integrate RStudio and LaTeX, especially the inclusion of well-formatted tables and nice-looking graphs and figures produced in RStudio and imported to LaTeX. To follow along you will need RStudio, MS Excel and LaTeX. Using tikzdevice to insert R Graphs into LaTeX I am a very visual thinker. If I want to understand a concept I usually and subconsciously try to visualise it. Therefore, more my PhD I tried to transport a lot of empirical insights by means of  visualization . These range from histograms, or violin plots to show distributions, over bargraphs including error bars to compare means, to interaction- or conditional effects of regression models. For quite a while it was very tedious to include such graphs in LaTeX documents. I tried several ways, like saving them as pdf and then including them in LaTeX as pdf, or any other file ...
Post a Comment
Read more

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...
Post a Comment
Read more